What is media mix modeling?
What is MMM? AKA media mix modeling
Media mix modeling (MMM) is a statistical analysis that utilizes aggregated data to measure a wide range of marketing activities to determine their impact on a company’s return on investment (ROI). MMM is also known as marketing mix modeling.
MMM measurement allows marketers to examine a wider range of changes, from digital to traditional, and factor in external influences like promotions, seasonality, press coverage, and more.
Below, you can see a marketing mix modeling example graph which displays the revenue contributions dependent on a specific mix of individual marketing channels. In this case, marketers would use these insights to see the generated ROI of their combined marketing efforts compared to the weekly costs of these efforts.
Questions an MMM model can answer
Media mix analysis can be used to answer marketing questions like the following:
- What’s the ROI of each of my marketing channels?
- Where should my ad spend go in the future?
- What is the impact of external factors on my company’s total revenue?
- How are earned, owned, and paid media contributing to my company’s ROI?
There are many more questions, specific to your app business, that an MMM model can answer. For a list of 10 questions an MMM model can answer for app marketers, and a complete guide on how to build a media mix model, check out our in-depth guide: Media mix modeling (MMM): The app marketer’s handbook.
The media mix modeling framework explained
An MMM framework encompasses the marketing channels in use, the amount of ad spend going into each channel, and the previous campaign results. The goal of this ratio is for a marketing team to be able to uncover which variables to use to impact the success of their future campaigns.
The MMM data marketers most often put into their marketing mix modeling feed includes:
Mobile attribution numbers: This is first-party data or data from Facebook, Google Ads, TikTok or any other self-attributing network (SAN) you partner with, or data from the SDKs for your mobile measurement partner (MMP) covering your digital advertising results.
Seasonality: Seasonality affects many mobile apps. From holiday trends to events like back to school, plugging seasonal data into your MMM feed can help you predict how different seasonal events can impact your marketing efforts.
Press: Although the power press coverage varies by app vertical, if your marketing team is pumping out quality bylines, press releases, and guest posts, press is worth measuring! We also recommend reviewing your app’s daily active users (DAUs) to see if and when press coverage affects engagement with your app.
Check out the image below for a diagram of the media mix modeling framework.
Challenges and opportunities in media mix modeling
As we predicted at the beginning of the year in Our top 5 mobile app marketing predictions for 2023, forecasting via media mix modeling will return. But before marketers dip their toes into the MMM measurement waters, many are trying to understand the marketing mix modeling techniques and challenges. Therefore, we’ve listed the challenges and benefits for you below.
Challenge 1: Complex
Yes, media mix analysis involves statistics. Therefore, setup will require partnering with either a data scientist familiar with media mix modeling tools or utilizing resources to get a marketing mix modeling software.
Initially, you’ll need to input a plethora of data into your marketing mix modeling feed. While the setup of your model may prove laborious, even if you are using an automated marketing mix modeling software, the results of the right setup will likely far outweigh the cost in resources.
Challenge 2: Doesn’t provide granular insights or cross-channel impact
Again, it’s essential to remember that an MMM framework provides marketers with an overview of their marketing activities. It is not used to drill-down to see how creatives are performing at a channel level or seeing how ads on one channel are affecting your campaigns on another.
However, if you partner with a mobile measurement and analytics platform like Adjust, you can zoom in to see how your campaigns are performing at a channel or creative level with our Mobile Attribution solution. Relatedly, you can see the impact of your channels on your other campaigns. For example, our CTV AdVision solution lets you see how your connected TV campaigns influence your other campaigns.
Now let’s talk about the benefits of media mix modeling.
When reviewing the marketing mix modeling techniques and challenges, one may feel overwhelmed. However, review the benefits below to see why MMM advertising framework may be worth it for your app business.
Benefit 1: Improved oversight and optimization
An MMM advertising framework ultimately allows marketers to get high-level insights into their specific marketing tactics over a long period of time. Having a holistic view of your marketing activities and trends in the market lets you see what’s working with your campaigns.
Additionally, the insights from MMM can reveal which factors are driving conversions for your mobile app. You can then visualize budgets with greater efficiency and better optimize ad spend for future campaigns.
Benefit 2: Enhanced targeting
Media mix modeling lets marketers run different campaign scenarios. For example, you can see how changing ad spend and/or targeting different audience segments can help you reach your Key Performance Indicators (KPIs). The more accurate user information you feed your model, the more you increase its ability to help you improve your targeting.
Benefit 3: Forecasting
The ability to forecast sales is one of the main attractions of a media mix analysis. The historical data fed to the model can be used to generate predictions of how successful different combinations of marketing efforts will be. Well-built models can provide channel lift as well as anticipated revenue and user engagement.
Adjust and media mix modeling
You can spend all the time in the world setting up your model, but if your data sources are not accurate, your modeling won’t be either. Let Adjust’s analytics solution Datascape collect all your digital marketing data into a centralized place, ensuring your data stays clean and accurate.
With our robust mobile measurement and analytics suite, you can easily supply your model with digital data as well as drill down into granular insights that you couldn’t get otherwise in media mix modeling.
Adjust is a pioneer in predictive analytics, with our pLTV (predictive lifetime value) product already being used in beta. As the name suggests, this feature helps marketers predict the future lifetime value of their users. We are excited about the tremendous potential of this innovative space, and are looking forward to guiding all of our clients through the capabilities of predictive analysis.
As part of Adjust’s next-gen measurement solutions, we’re developing a media mix modeling solution. To learn more request your personalized demo for the MMP over 135,000 apps trust for their app marketing insights.
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